| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 34 | | adverbTagCount | 1 | | adverbTags | | 0 | "She gestured vaguely [vaguely]" |
| | dialogueSentences | 63 | | tagDensity | 0.54 | | leniency | 1 | | rawRatio | 0.029 | | effectiveRatio | 0.029 | |
| 96.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1556 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 67.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1556 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "scanning" | | 1 | "pulse" | | 2 | "silence" | | 3 | "charm" | | 4 | "perfect" | | 5 | "could feel" | | 6 | "weight" | | 7 | "traced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 124 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 1549 | | ratio | 0.006 | | matches | | 0 | "I can't be what you need. Don't look for me." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 870 | | uniqueNames | 9 | | maxNameDensity | 0.8 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Moreau | 1 | | Ptolemy | 2 | | Eva | 3 | | Rory | 7 | | Turned | 1 | | Lucien | 7 | | Whitechapel | 1 | | Evan | 1 | | Faced | 1 |
| | persons | | 0 | "Moreau" | | 1 | "Ptolemy" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Turned" | | 5 | "Lucien" | | 6 | "Evan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like breaking something open" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1549 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 124 | | matches | | 0 | "scrolls that covered" | | 1 | "realized that locks" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 34.42 | | std | 23.87 | | cv | 0.693 | | sampleLengths | | 0 | 50 | | 1 | 4 | | 2 | 53 | | 3 | 7 | | 4 | 29 | | 5 | 57 | | 6 | 19 | | 7 | 11 | | 8 | 58 | | 9 | 62 | | 10 | 42 | | 11 | 3 | | 12 | 45 | | 13 | 18 | | 14 | 45 | | 15 | 44 | | 16 | 8 | | 17 | 29 | | 18 | 37 | | 19 | 63 | | 20 | 13 | | 21 | 79 | | 22 | 45 | | 23 | 88 | | 24 | 8 | | 25 | 36 | | 26 | 20 | | 27 | 39 | | 28 | 7 | | 29 | 1 | | 30 | 74 | | 31 | 31 | | 32 | 13 | | 33 | 54 | | 34 | 29 | | 35 | 19 | | 36 | 12 | | 37 | 9 | | 38 | 96 | | 39 | 32 | | 40 | 46 | | 41 | 15 | | 42 | 55 | | 43 | 37 | | 44 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 77.30% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 163 | | matches | | 0 | "was ordering" | | 1 | "was shouting" | | 2 | "were shaking" |
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| 96.77% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 124 | | ratio | 0.016 | | matches | | 0 | "She'd seen him get his hands dirty, night after night, pulling strings and brokering deals and once—she still couldn't think about that once—dragging her out of a warehouse in Whitechapel with a demon's claws still smoking in the doorframe behind them." | | 1 | "His eyes—one amber, one black—held hers with a desperation that made her chest ache." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 830 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.03975903614457831 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004819277108433735 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 124 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 124 | | mean | 12.49 | | std | 11.51 | | cv | 0.921 | | sampleLengths | | 0 | 4 | | 1 | 3 | | 2 | 28 | | 3 | 6 | | 4 | 9 | | 5 | 4 | | 6 | 26 | | 7 | 5 | | 8 | 12 | | 9 | 6 | | 10 | 4 | | 11 | 4 | | 12 | 3 | | 13 | 5 | | 14 | 4 | | 15 | 4 | | 16 | 11 | | 17 | 5 | | 18 | 21 | | 19 | 14 | | 20 | 3 | | 21 | 19 | | 22 | 15 | | 23 | 4 | | 24 | 5 | | 25 | 6 | | 26 | 33 | | 27 | 14 | | 28 | 1 | | 29 | 6 | | 30 | 4 | | 31 | 9 | | 32 | 8 | | 33 | 7 | | 34 | 6 | | 35 | 28 | | 36 | 4 | | 37 | 3 | | 38 | 2 | | 39 | 37 | | 40 | 3 | | 41 | 7 | | 42 | 4 | | 43 | 19 | | 44 | 15 | | 45 | 4 | | 46 | 6 | | 47 | 8 | | 48 | 17 | | 49 | 28 |
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| 51.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3548387096774194 | | totalSentences | 124 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 81 | | matches | | 0 | "Then the second." | | 1 | "Too dark, too formal." | | 2 | "Then she'd stopped, because Eva" | | 3 | "Instead she said," |
| | ratio | 0.049 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 81 | | matches | | 0 | "She didn't look at the" | | 1 | "She already knew who stood" | | 2 | "He'd once told her the" | | 3 | "She'd laughed and called him" | | 4 | "He didn't smile" | | 5 | "He only wore black like" | | 6 | "He stepped inside before she" | | 7 | "He always hated this place," | | 8 | "He said it flat, like" | | 9 | "He moved past her, into" | | 10 | "She hadn't seen him in" | | 11 | "She'd looked anyway." | | 12 | "He cut her off, and" | | 13 | "She had seen." | | 14 | "She'd seen him get his" | | 15 | "He said it without heat," | | 16 | "She did have four deadbolts." | | 17 | "She'd installed the fourth herself," | | 18 | "He finally looked at her," | | 19 | "He broke off, jaw tight" |
| | ratio | 0.568 | |
| 3.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 81 | | matches | | 0 | "The third deadbolt clicked." | | 1 | "Rory counted the seconds between" | | 2 | "She didn't look at the" | | 3 | "She already knew who stood" | | 4 | "The door swung open." | | 5 | "Lucien Moreau filled the frame" | | 6 | "He'd once told her the" | | 7 | "She'd laughed and called him" | | 8 | "That had been before." | | 9 | "He didn't smile" | | 10 | "The suit was wrong." | | 11 | "He only wore black like" | | 12 | "He stepped inside before she" | | 13 | "The flat smelled of Ptolemy's" | | 14 | "Lucien's nose wrinkled." | | 15 | "He always hated this place," | | 16 | "He said it flat, like" | | 17 | "Rory's hand found the doorframe." | | 18 | "He moved past her, into" | | 19 | "The amber eye caught the" |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 3 | | matches | | 0 | "He stepped closer, close enough that she could smell him, bergamot and something darker, something that might have been ozone or old magic." | | 1 | "His eyes—one amber, one black—held hers with a desperation that made her chest ache." | | 2 | "His thumb found the crescent scar, traced it with a gentleness that made her breath catch." |
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| 80.88% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 34 | | uselessAdditionCount | 3 | | matches | | 0 | "He broke, jaw tight" | | 1 | "He laughed, hollow" | | 2 | "He laughed, broken" |
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| 86.51% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 4 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "He was shouting now (be shout)" | | 2 | "He laughed (laugh)" | | 3 | "he whispered (whisper)" |
| | dialogueSentences | 63 | | tagDensity | 0.19 | | leniency | 0.381 | | rawRatio | 0.333 | | effectiveRatio | 0.127 | |